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|Title:||Visual distance measures for object retrieval|
|Citation:||Proceedings of the International Conference on Digital Image Computing Techniques and Applications, held in Fremantle, 3-5 December, 2012: pp.1-8|
|Conference Name:||International Conference on Digital Image Computing Techniques and Applications (2012 : Fremantle)|
|Yanzhi Chen, Anthony Dick and Xi Li|
|Abstract:||This paper describes an enhanced visual distance measure for image features, and evaluates its effect on object retrieval accuracy for several standard datasets. The measure incorporates semantic proximity information that is automatically extracted from each dataset in an offline step. It is designed to overcome errors introduced by feature detection and quantization in the “bag-of-words” model. We define a cross-word image similarity measure using this visual word distance, and show that it improves object retrieval precision for several datasets. It involves minimal additional query time cost, and can be embedded into any object retrieval method that uses a “bag-of-words” model.|
|Keywords:||Atmospheric measurements; buildings; particle measurements; semantics; standards; vectors; visualisation|
|Rights:||© 2012 IEEE|
|Appears in Collections:||Computer Science publications|
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